III: Small: Distributed Semantic Information Processing Applied to Camera Sensor Networks

Sponsor: National Science Foundation

Award Number: IIS-1717656

Abstract:

In many applications, sensor networks can be used to monitor large geographical regions. This typically produces large quantities of data that need to be associated, summarized and classified in order to arrive to a semantically meaningful descriptions of the phenomena being monitored. The long-term guiding vision of this project is a distributed network that can perform this analysis autonomously, over long periods of times, and in a scalable way. As a concrete application, this research focuses on smart camera networks with nodes that are either static or part of robotic agents. The planned work will result in systems that are more efficient, accurate, and resilient. The algorithms developed will find wide applications, including in security (continuously detecting suspicious individuals in real time) and the Internet of Things. As part of the broader impacts, the project will produce educational material to explain the scientific results of the project to a K12 audience.

This research will introduce a novel framework for consistently, efficiently and reliably extracting semantic information across a network of smart cameras. The project will revolve around three pillars: (i) solving the consistent multi-camera feature matching problem: find if a feature seen at a node has been seen by other camera nodes, and if yes, find the most consistent correspondences using a variation of decentralized hashing; (ii) autonomously finding emerging patterns (e.g., images from different individuals) through feature clustering, using a distributed algorithm for computing k-medoids; (iii) employing a new distributed decision tree inference paradigm that allows the network to take consistent decisions about the world (for example, the identity of an individual) while spreading computations across the network. The project website will provide access to the results of the work, including papers, downloadable code, and educational material.

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